Universal association between depressive symptoms and social-network structures in the workplace

An unhealthy communication structure at a workplace can adversely affect the mental health of employees. However, little is known about the relationship between communication structures in the workplace and the mental health of employees. Here, we evaluated the face-to-face interaction network among employees (N = 449) in a variety of real-world working environments by using wearable devices and investigated the relationship between social network characteristics and depressive symptoms. We found that the cohesive interaction structure surrounding each individual was negatively correlated with depressive symptoms: a universal relationship regardless of occupation type. This correlation was evident at the group scale and was strongly related to active interactions with abundant body movement. Our findings provide a quantitative and collective perspective on taking a systematic approach to workplace depression, and they suggest that the mental health of employees needs to be addressed systematically, not only individually.


Supplementary note 1 -Correlation between social network characteristics and depression scale in the community unit.
Fig. S1 shows the relationship between social network characteristics and depression scale in the community unit. The method used for the analysis is the same as Fig. 3c. In addition to Clustering and Clustering w(B), the correlation observed in Eigen, Eigen w, and Closeness was stronger than that of randomly constructed community (Blue) and randomly constructed community within each organization (Green). Clustering and Clustering w(B) are partly related to Eigen, Eigen w, and Closeness in that highly clustering structure reduces the length of paths to other nodes and shares influence with each other. However, these indicators (Eigen, Eigen w, and Closeness) are difficult to interpret by themselves compared to Clustering and Clustering w(B) in that they are not valid for individual unit as shown in Fig. 2a, and their correlation with depression scale is weaker than that of Clustering and Clustering w(B). In addition, while these indicators are useful for relative comparison within a specific network, there is a disadvantage in that it is difficult to directly compare indices calculated in different networks (especially networks of very different sizes).

Supplementary note 2 -Observation period.
To investigate the effect of observation period on the correlation between the Clustering w(B) and depression scale, we composed the interaction data of N consecutive days of each organization into one network, and investigated the correlation between the Clustering w(B) and the depression scale observed in each network (Fig. S2a). Fig. S2b shows the change in the Pearson's correlations of each sample according to the number of consecutive days (N). As N increases, the correlation clearly shows a negative trend, and become significantly lower than 0 over 3 consecutive days (N=3; 95% CI -0.065 to -0.0035). As a result of the same analysis for each organization (Fig. S2c), significant negative trends are observed in 8 out of 10 organizations from observations over 4 days, and become stronger as the observation period increased. These results show that the correlation between Clustering w(B) and depression scale reflects the characteristics of chronic interaction between members at the workplace. First, from the results of Fig. S3a and S3c, Clustering w(B) and Clustering, which showed the strongest negative correlation in the total score, showed a high overall negative correlation even when looking at each items. Interestingly, it can be seen that there is a relatively strong correlation with the items corresponding to somatic symptoms: e.g. 7 -I felt that everything I did was an effort, 11 -My sleep was restless, 13 -I talked less than usual. 20 -I could not "get going".
Another interesting point is that

Supplementary method 1 -the criterion for empty intervals between interactions.
We quantify the weight of the relationship as the number of interactions. However, when we actually looked at the interactions (Fig. S4a), we found that short empty intervals frequently appeared between the interactions (Fig. S4b).
These empty intervals may be caused by misalignment between the infrared sensors of the wearable sensors, or may actually be caused by intermittent interaction. However, we judged that it is more reasonable to regard the interactions that occurred over such a short interval as a continuous interaction rather than interactions in a completely new context.
We set the interval less than 5 minutes as the criterion for imputation, which is not a long time perceptually, and appears with a high frequency in the data. We think that the frequency of interactions is counted more reasonably through this criterion (Fig. S4a), and we confirmed that our results, the correlation between depressive symptoms and Clustering, Clustering w(B), are robust regardless of the specific threshold of '5 minutes' (Fig. S4c). Compared with the modularity of unweighted networks, modularity using weight of links can extract community structures with higher resolution and accuracy by using additional information on the frequency of interactions 2 . On the other hand, there is a possibility that many structural features (e.g. clustering coefficient) may be neglected in the extracted community structures, as the connections between people connected by relatively low weight links do not become important.
Therefore we searched for a modular community structure while including a large share of the clustering coefficients of the members by adjusting the power of the weight of links in modularity. The modularity we used for community detection is as follows.
Here, is a parameter for adjusting the power of the weight. Accordingly, W = ∑ , /2 and ̃= ∑ are  increases, although the resolution of detection is increased (Fig. S5b) but there is no significant increase in the modularity of extracted community structures (Fig. S5a). In addition, the member's proportion of clustering coefficients, degree, and weight within the community decreases (Fig. S5c, d), while the proportion of Weight and Clustering w(B) within community increase very slightly in the beginning. From these analyses, we determined as 0.25 to extract the community structures which is sufficiently modularized when considering weight of links and where the proportion of clustering coefficients of members is higher inside than outside. shows the robustness of the correlation at community scale, and suggests that this correlation is evident in a team unit that shared clustering structure internally rather than just a high-resolution team unit entangled with strong weight of links.